{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## _*LiH plot using NumPyMinimumEigensolver*_\n",
    "\n",
    "This notebook demonstrates using Qiskit Chemistry to plot graphs of the ground state energy and dipole moments of a Lithium Hydride (LiH) molecule over a range of inter-atomic distances.\n",
    "   \n",
    "This notebook has been written to use the PYSCF chemistry driver. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing step 20 --- complete\n",
      "Distances:  [1.25  1.275 1.3   1.325 1.35  1.375 1.4   1.425 1.45  1.475 1.5   1.525\n",
      " 1.55  1.575 1.6   1.625 1.65  1.675 1.7   1.725 1.75 ]\n",
      "Energies: [-7.86021175 -7.86413664 -7.86756329 -7.87052961 -7.87307044 -7.87521786\n",
      " -7.87700149 -7.87844868 -7.87958474 -7.88043316 -7.88101572 -7.88135266\n",
      " -7.88146285 -7.88136385 -7.88107204 -7.88060273 -7.8799702  -7.87918784\n",
      " -7.87826817 -7.87722291 -7.87606307]\n",
      "Dipole moments: [1.85348096 1.85204573 1.85067375 1.84935828 1.84809268 1.84687002\n",
      " 1.84568265 1.84452191 1.84337791 1.84223932 1.84109328 1.83992524\n",
      " 1.83871893 1.8374563  1.83611747 1.83468076 1.83312267 1.83141785\n",
      " 1.82953923 1.82745794 1.82514338]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pylab\n",
    "from qiskit.aqua.algorithms import NumPyMinimumEigensolver\n",
    "from qiskit.chemistry.drivers import PySCFDriver\n",
    "from qiskit.chemistry.core import Hamiltonian\n",
    "\n",
    "# Note: In order to allow this to run reasonably quickly it takes advantage\n",
    "#       of the ability to freeze core orbitals and remove unoccupied virtual\n",
    "#       orbitals to reduce the size of the problem. Freeze core can always\n",
    "#       be used, but be very cautious when removing unoccupied orbitals.\n",
    "#  \n",
    "\n",
    "molecule = 'Li .0 .0 -{0}; H .0 .0 {0}'\n",
    "\n",
    "start = 1.25 # Start distance\n",
    "by    = 0.5  # How much to increase distance by\n",
    "steps = 20   # Number of steps to increase by\n",
    "energies  = np.empty(steps+1)\n",
    "distances = np.empty(steps+1)\n",
    "dipoles   = np.empty(steps+1)\n",
    "\n",
    "print('Processing step __', end='')\n",
    "for i in range(steps+1):\n",
    "    print('\\b\\b{:2d}'.format(i), end='', flush=True)\n",
    "    d = start + i*by/steps \n",
    "    driver = PySCFDriver(molecule.format(d/2), basis='sto3g')\n",
    "    qmolecule = driver.run()\n",
    "    operator = Hamiltonian(freeze_core=True, orbital_reduction=[-3, -2])\n",
    "    qubit_op, aux_ops = operator.run(qmolecule)\n",
    "    result = NumPyMinimumEigensolver(qubit_op, aux_operators=aux_ops).run()\n",
    "    result = operator.process_algorithm_result(result)\n",
    "    distances[i] = d\n",
    "    energies[i] = result.energy\n",
    "    dipoles[i]  = result.total_dipole_moment\n",
    "print(' --- complete')\n",
    "\n",
    "print('Distances: ', distances)\n",
    "print('Energies:', energies)\n",
    "print('Dipole moments:', dipoles)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pylab.plot(distances, energies)\n",
    "pylab.xlabel('Interatomic distance')\n",
    "pylab.ylabel('Energy')\n",
    "pylab.title('LiH Ground State Energy');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pylab.plot(distances, dipoles)\n",
    "pylab.xlabel('Interatomic distance')\n",
    "pylab.ylabel('Moment a.u')\n",
    "pylab.title('LiH Dipole Moment');"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
